SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles

• URL: http://arxiv.org/abs/2404.18411v2
• Date: Sat, 01 Feb 2025 04:59:36 GMT
• Title: SeePerSea: Multi-modal Perception Dataset of In-water Objects for Autonomous Surface Vehicles
• Authors: Mingi Jeong, Arihant Chadda, Ziang Ren, Luyang Zhao, Haowen Liu, Monika Roznere, Aiwei Zhang, Yitao Jiang, Sabriel Achong, Samuel Lensgraf, Alberto Quattrini Li,
• Abstract summary: This paper introduces the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation.<n>It focuses on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs)
• Score: 10.732732686425308
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper introduces the first publicly accessible labeled multi-modal perception dataset for autonomous maritime navigation, focusing on in-water obstacles within the aquatic environment to enhance situational awareness for Autonomous Surface Vehicles (ASVs). This dataset, collected over 4 years and consisting of diverse objects encountered under varying environmental conditions, aims to bridge the research gap in autonomous surface vehicles by providing a multi-modal, annotated, and ego-centric perception dataset, for object detection and classification. We also show the applicability of the proposed dataset by training deep learning-based open-source perception algorithms that have shown success. We expect that our dataset will contribute to development of the marine autonomy pipelines and marine (field) robotics. This dataset is opensource and can be found at https://seepersea.github.io/.

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